wehub-resource-sync
209 lines
9.0 KiB
C++
209 lines
9.0 KiB
C++
/* Copyright 2018 The TensorFlow Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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==============================================================================*/
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#include <stdint.h>
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#include <vector>
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#include <gtest/gtest.h>
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#include "tensorflow/lite/kernels/test_util.h"
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#include "tensorflow/lite/schema/schema_generated.h"
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namespace tflite {
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namespace {
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using ::testing::ElementsAre;
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using ::testing::ElementsAreArray;
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template <typename T>
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class ReverseSequenceOpModel : public SingleOpModel {
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public:
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ReverseSequenceOpModel(const TensorData& input, const TensorData& seq_lengths,
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int seq_dim, int batch_dim) {
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input_ = AddInput(input);
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seq_lengths_ = AddInput(seq_lengths);
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output_ = AddOutput({input.type, {}});
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SetBuiltinOp(
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BuiltinOperator_REVERSE_SEQUENCE, BuiltinOptions_ReverseSequenceOptions,
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CreateReverseSequenceOptions(builder_, seq_dim, batch_dim).Union());
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BuildInterpreter({GetShape(input_)});
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}
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int input() { return input_; }
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int seq_lengths() { return seq_lengths_; }
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std::vector<T> GetOutput() { return ExtractVector<T>(output_); }
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std::vector<int> GetOutputShape() { return GetTensorShape(output_); }
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private:
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int input_;
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int seq_lengths_;
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int output_;
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};
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// float32 tests
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TEST(ReverseSequenceOpTest, FloatSeqDimIsGreater) {
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ReverseSequenceOpModel<float> model({TensorType_FLOAT32, {4, 3, 2}},
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{TensorType_INT32, {4}}, 1, 0);
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model.PopulateTensor<float>(model.input(),
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 2, 3, 3});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 9, 10, 7, 8, 11, 12,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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TEST(ReverseSequenceOpTest, FloatBatchDimIsGreater) {
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ReverseSequenceOpModel<float> model({TensorType_FLOAT32, {4, 3, 2}},
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{TensorType_INT32, {2}}, 0, 2);
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model.PopulateTensor<float>(model.input(),
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{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 4});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({13, 20, 15, 22, 17, 24, 7, 14, 9, 16, 11, 18, 1,
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8, 3, 10, 5, 12, 19, 2, 21, 4, 23, 6}));
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}
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// int32 tests
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TEST(ReverseSequenceOpTest, Int32SeqDimIsGreater) {
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ReverseSequenceOpModel<int32_t> model({TensorType_INT32, {4, 3, 2}},
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{TensorType_INT32, {4}}, 1, 0);
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 2, 3, 3});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 9, 10, 7, 8, 11, 12,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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TEST(ReverseSequenceOpTest, Int32BatchDimIsGreater) {
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ReverseSequenceOpModel<int32_t> model({TensorType_INT32, {4, 3, 2}},
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{TensorType_INT32, {2}}, 0, 2);
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model.PopulateTensor<int32_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 4});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({13, 20, 15, 22, 17, 24, 7, 14, 9, 16, 11, 18, 1,
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8, 3, 10, 5, 12, 19, 2, 21, 4, 23, 6}));
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}
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// int64 tests
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TEST(ReverseSequenceOpTest, Int64SeqDimIsGreater) {
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ReverseSequenceOpModel<int64_t> model({TensorType_INT64, {4, 3, 2}},
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{TensorType_INT32, {4}}, 1, 0);
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model.PopulateTensor<int64_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 2, 3, 3});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 9, 10, 7, 8, 11, 12,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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TEST(ReverseSequenceOpTest, Int64BatchDimIsGreater) {
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ReverseSequenceOpModel<int64_t> model({TensorType_INT64, {4, 3, 2}},
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{TensorType_INT32, {2}}, 0, 2);
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model.PopulateTensor<int64_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 4});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({13, 20, 15, 22, 17, 24, 7, 14, 9, 16, 11, 18, 1,
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8, 3, 10, 5, 12, 19, 2, 21, 4, 23, 6}));
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}
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// uint8 tests
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TEST(ReverseSequenceOpTest, Uint8SeqDimIsGreater) {
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ReverseSequenceOpModel<uint8_t> model({TensorType_UINT8, {4, 3, 2}},
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{TensorType_INT32, {4}}, 1, 0);
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model.PopulateTensor<uint8_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 2, 3, 3});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 9, 10, 7, 8, 11, 12,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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TEST(ReverseSequenceOpTest, Uint8BatchDimIsGreater) {
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ReverseSequenceOpModel<uint8_t> model({TensorType_UINT8, {4, 3, 2}},
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{TensorType_INT32, {2}}, 0, 2);
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model.PopulateTensor<uint8_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 4});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({13, 20, 15, 22, 17, 24, 7, 14, 9, 16, 11, 18, 1,
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8, 3, 10, 5, 12, 19, 2, 21, 4, 23, 6}));
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}
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// int16 tests
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TEST(ReverseSequenceOpTest, Int16SeqDimIsGreater) {
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ReverseSequenceOpModel<int16_t> model({TensorType_INT16, {4, 3, 2}},
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{TensorType_INT32, {4}}, 1, 0);
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model.PopulateTensor<int16_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 2, 3, 3});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(
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model.GetOutput(),
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ElementsAreArray({5, 6, 3, 4, 1, 2, 9, 10, 7, 8, 11, 12,
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17, 18, 15, 16, 13, 14, 23, 24, 21, 22, 19, 20}));
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}
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TEST(ReverseSequenceOpTest, Int16BatchDimIsGreater) {
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ReverseSequenceOpModel<int16_t> model({TensorType_INT16, {4, 3, 2}},
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{TensorType_INT32, {2}}, 0, 2);
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model.PopulateTensor<int16_t>(
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model.input(), {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
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13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24});
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model.PopulateTensor<int32_t>(model.seq_lengths(), {3, 4});
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ASSERT_EQ(model.Invoke(), kTfLiteOk);
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EXPECT_THAT(model.GetOutputShape(), ElementsAre(4, 3, 2));
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EXPECT_THAT(model.GetOutput(),
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ElementsAreArray({13, 20, 15, 22, 17, 24, 7, 14, 9, 16, 11, 18, 1,
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8, 3, 10, 5, 12, 19, 2, 21, 4, 23, 6}));
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}
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} // namespace
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} // namespace tflite
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